Overview

Dataset statistics

Number of variables18
Number of observations31647
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.7 MiB
Average record size in memory685.2 B

Variable types

Numeric8
Categorical6
Boolean4

Alerts

ID is highly overall correlated with housing and 6 other fieldsHigh correlation
pdays is highly overall correlated with ID and 1 other fieldsHigh correlation
previous is highly overall correlated with pdaysHigh correlation
housing is highly overall correlated with ID and 1 other fieldsHigh correlation
contact is highly overall correlated with ID and 1 other fieldsHigh correlation
month is highly overall correlated with ID and 3 other fieldsHigh correlation
poutcome is highly overall correlated with ID and 1 other fieldsHigh correlation
age is highly overall correlated with jobHigh correlation
job is highly overall correlated with age and 1 other fieldsHigh correlation
education is highly overall correlated with jobHigh correlation
day is highly overall correlated with ID and 1 other fieldsHigh correlation
subscribed is highly overall correlated with IDHigh correlation
previous is highly skewed (γ1 = 49.30234792)Skewed
ID is uniformly distributedUniform
ID has unique valuesUnique
balance has 2470 (7.8%) zerosZeros
previous has 25924 (81.9%) zerosZeros

Reproduction

Analysis started2023-01-12 13:54:23.989565
Analysis finished2023-01-12 13:55:13.470051
Duration49.48 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct31647
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22563.972
Minimum2
Maximum45211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size247.4 KiB
2023-01-12T08:55:14.058054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2251.9
Q111218
median22519
Q333879.5
95-th percentile42964.1
Maximum45211
Range45209
Interquartile range (IQR)22661.5

Descriptive statistics

Standard deviation13075.937
Coefficient of variation (CV)0.5795051
Kurtosis-1.2043412
Mean22563.972
Median Absolute Deviation (MAD)11330
Skewness0.0058507956
Sum7.1408203 × 108
Variance1.7098013 × 108
MonotonicityNot monotonic
2023-01-12T08:55:14.830052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26110 1
 
< 0.1%
13339 1
 
< 0.1%
39681 1
 
< 0.1%
15135 1
 
< 0.1%
26037 1
 
< 0.1%
41484 1
 
< 0.1%
2281 1
 
< 0.1%
31869 1
 
< 0.1%
42096 1
 
< 0.1%
15737 1
 
< 0.1%
Other values (31637) 31637
> 99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
45211 1
< 0.1%
45210 1
< 0.1%
45209 1
< 0.1%
45208 1
< 0.1%
45207 1
< 0.1%
45205 1
< 0.1%
45204 1
< 0.1%
45203 1
< 0.1%
45200 1
< 0.1%
45199 1
< 0.1%

age
Real number (ℝ)

Distinct76
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.957247
Minimum18
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size247.4 KiB
2023-01-12T08:55:15.572052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile27
Q133
median39
Q348
95-th percentile59
Maximum95
Range77
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.625134
Coefficient of variation (CV)0.25942013
Kurtosis0.29797526
Mean40.957247
Median Absolute Deviation (MAD)7
Skewness0.68160678
Sum1296174
Variance112.89348
MonotonicityNot monotonic
2023-01-12T08:55:16.049053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 1457
 
4.6%
31 1417
 
4.5%
33 1406
 
4.4%
34 1321
 
4.2%
35 1314
 
4.2%
36 1245
 
3.9%
30 1219
 
3.9%
37 1181
 
3.7%
39 1079
 
3.4%
38 985
 
3.1%
Other values (66) 19023
60.1%
ValueCountFrequency (%)
18 8
 
< 0.1%
19 22
 
0.1%
20 39
 
0.1%
21 48
 
0.2%
22 86
 
0.3%
23 142
 
0.4%
24 212
 
0.7%
25 366
1.2%
26 564
1.8%
27 627
2.0%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 1
 
< 0.1%
93 1
 
< 0.1%
92 1
 
< 0.1%
90 1
 
< 0.1%
89 2
 
< 0.1%
88 2
 
< 0.1%
87 2
 
< 0.1%
86 8
< 0.1%
84 5
< 0.1%

job
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
blue-collar
6842 
management
6639 
technician
5307 
admin.
3631 
services
2903 
Other values (7)
6325 

Length

Max length13
Median length12
Mean length9.487408
Min length6

Characters and Unicode

Total characters300248
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadmin.
2nd rowunknown
3rd rowservices
4th rowmanagement
5th rowtechnician

Common Values

ValueCountFrequency (%)
blue-collar 6842
21.6%
management 6639
21.0%
technician 5307
16.8%
admin. 3631
11.5%
services 2903
9.2%
retired 1574
 
5.0%
self-employed 1123
 
3.5%
entrepreneur 1008
 
3.2%
unemployed 905
 
2.9%
housemaid 874
 
2.8%
Other values (2) 841
 
2.7%

Length

2023-01-12T08:55:16.544050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
blue-collar 6842
21.6%
management 6639
21.0%
technician 5307
16.8%
admin 3631
11.5%
services 2903
9.2%
retired 1574
 
5.0%
self-employed 1123
 
3.5%
entrepreneur 1008
 
3.2%
unemployed 905
 
2.9%
housemaid 874
 
2.8%
Other values (2) 841
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e 45101
15.0%
n 31697
10.6%
a 29932
10.0%
l 23677
 
7.9%
c 20359
 
6.8%
m 19811
 
6.6%
i 19596
 
6.5%
r 15917
 
5.3%
t 15798
 
5.3%
u 10470
 
3.5%
Other values (14) 67890
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 288652
96.1%
Dash Punctuation 7965
 
2.7%
Other Punctuation 3631
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 45101
15.6%
n 31697
11.0%
a 29932
10.4%
l 23677
8.2%
c 20359
 
7.1%
m 19811
 
6.9%
i 19596
 
6.8%
r 15917
 
5.5%
t 15798
 
5.5%
u 10470
 
3.6%
Other values (12) 56294
19.5%
Dash Punctuation
ValueCountFrequency (%)
- 7965
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 288652
96.1%
Common 11596
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 45101
15.6%
n 31697
11.0%
a 29932
10.4%
l 23677
8.2%
c 20359
 
7.1%
m 19811
 
6.9%
i 19596
 
6.8%
r 15917
 
5.5%
t 15798
 
5.5%
u 10470
 
3.6%
Other values (12) 56294
19.5%
Common
ValueCountFrequency (%)
- 7965
68.7%
. 3631
31.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300248
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 45101
15.0%
n 31697
10.6%
a 29932
10.0%
l 23677
 
7.9%
c 20359
 
6.8%
m 19811
 
6.6%
i 19596
 
6.5%
r 15917
 
5.3%
t 15798
 
5.3%
u 10470
 
3.5%
Other values (14) 67890
22.6%

marital
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
married
19095 
single
8922 
divorced
3630 

Length

Max length8
Median length7
Mean length6.8327804
Min length6

Characters and Unicode

Total characters216237
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowdivorced
5th rowmarried

Common Values

ValueCountFrequency (%)
married 19095
60.3%
single 8922
28.2%
divorced 3630
 
11.5%

Length

2023-01-12T08:55:16.959051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T08:55:17.403050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
married 19095
60.3%
single 8922
28.2%
divorced 3630
 
11.5%

Most occurring characters

ValueCountFrequency (%)
r 41820
19.3%
i 31647
14.6%
e 31647
14.6%
d 26355
12.2%
m 19095
8.8%
a 19095
8.8%
s 8922
 
4.1%
n 8922
 
4.1%
g 8922
 
4.1%
l 8922
 
4.1%
Other values (3) 10890
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 216237
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 41820
19.3%
i 31647
14.6%
e 31647
14.6%
d 26355
12.2%
m 19095
8.8%
a 19095
8.8%
s 8922
 
4.1%
n 8922
 
4.1%
g 8922
 
4.1%
l 8922
 
4.1%
Other values (3) 10890
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 216237
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 41820
19.3%
i 31647
14.6%
e 31647
14.6%
d 26355
12.2%
m 19095
8.8%
a 19095
8.8%
s 8922
 
4.1%
n 8922
 
4.1%
g 8922
 
4.1%
l 8922
 
4.1%
Other values (3) 10890
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 216237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 41820
19.3%
i 31647
14.6%
e 31647
14.6%
d 26355
12.2%
m 19095
8.8%
a 19095
8.8%
s 8922
 
4.1%
n 8922
 
4.1%
g 8922
 
4.1%
l 8922
 
4.1%
Other values (3) 10890
 
5.0%

education
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
secondary
16224 
tertiary
9301 
primary
4808 
unknown
 
1314

Length

Max length9
Median length9
Mean length8.3192088
Min length7

Characters and Unicode

Total characters263278
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowsecondary
3rd rowsecondary
4th rowtertiary
5th rowsecondary

Common Values

ValueCountFrequency (%)
secondary 16224
51.3%
tertiary 9301
29.4%
primary 4808
 
15.2%
unknown 1314
 
4.2%

Length

2023-01-12T08:55:17.742053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T08:55:18.211052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
secondary 16224
51.3%
tertiary 9301
29.4%
primary 4808
 
15.2%
unknown 1314
 
4.2%

Most occurring characters

ValueCountFrequency (%)
r 44442
16.9%
a 30333
11.5%
y 30333
11.5%
e 25525
9.7%
n 20166
7.7%
t 18602
7.1%
o 17538
 
6.7%
s 16224
 
6.2%
c 16224
 
6.2%
d 16224
 
6.2%
Other values (6) 27667
10.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 263278
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 44442
16.9%
a 30333
11.5%
y 30333
11.5%
e 25525
9.7%
n 20166
7.7%
t 18602
7.1%
o 17538
 
6.7%
s 16224
 
6.2%
c 16224
 
6.2%
d 16224
 
6.2%
Other values (6) 27667
10.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 263278
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 44442
16.9%
a 30333
11.5%
y 30333
11.5%
e 25525
9.7%
n 20166
7.7%
t 18602
7.1%
o 17538
 
6.7%
s 16224
 
6.2%
c 16224
 
6.2%
d 16224
 
6.2%
Other values (6) 27667
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 263278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 44442
16.9%
a 30333
11.5%
y 30333
11.5%
e 25525
9.7%
n 20166
7.7%
t 18602
7.1%
o 17538
 
6.7%
s 16224
 
6.2%
c 16224
 
6.2%
d 16224
 
6.2%
Other values (6) 27667
10.5%

default
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
False
31062 
True
 
585
ValueCountFrequency (%)
False 31062
98.2%
True 585
 
1.8%
2023-01-12T08:55:18.633052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

balance
Real number (ℝ)

Distinct6326
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1363.8903
Minimum-8019
Maximum102127
Zeros2470
Zeros (%)7.8%
Negative2665
Negative (%)8.4%
Memory size247.4 KiB
2023-01-12T08:55:19.144053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-8019
5-th percentile-173
Q173
median450
Q31431
95-th percentile5768
Maximum102127
Range110146
Interquartile range (IQR)1358

Descriptive statistics

Standard deviation3028.3043
Coefficient of variation (CV)2.2203431
Kurtosis126.45128
Mean1363.8903
Median Absolute Deviation (MAD)450
Skewness7.9956956
Sum43163035
Variance9170626.9
MonotonicityNot monotonic
2023-01-12T08:55:19.533051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2470
 
7.8%
1 137
 
0.4%
2 109
 
0.3%
4 95
 
0.3%
3 88
 
0.3%
5 78
 
0.2%
6 62
 
0.2%
8 52
 
0.2%
10 48
 
0.2%
23 48
 
0.2%
Other values (6316) 28460
89.9%
ValueCountFrequency (%)
-8019 1
< 0.1%
-6847 1
< 0.1%
-4057 1
< 0.1%
-3372 1
< 0.1%
-3058 1
< 0.1%
-2712 1
< 0.1%
-2604 1
< 0.1%
-2282 1
< 0.1%
-2122 1
< 0.1%
-2082 1
< 0.1%
ValueCountFrequency (%)
102127 1
< 0.1%
81204 1
< 0.1%
66721 1
< 0.1%
66653 1
< 0.1%
58932 1
< 0.1%
58544 1
< 0.1%
57435 1
< 0.1%
56831 1
< 0.1%
52587 2
< 0.1%
52527 1
< 0.1%

housing
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
True
17584 
False
14063 
ValueCountFrequency (%)
True 17584
55.6%
False 14063
44.4%
2023-01-12T08:55:19.839056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

loan
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
False
26516 
True
5131 
ValueCountFrequency (%)
False 26516
83.8%
True 5131
 
16.2%
2023-01-12T08:55:20.078052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

contact
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
cellular
20423 
unknown
9177 
telephone
2047 

Length

Max length9
Median length8
Mean length7.7747022
Min length7

Characters and Unicode

Total characters246046
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowcellular
3rd rowcellular
4th rowcellular
5th rowcellular

Common Values

ValueCountFrequency (%)
cellular 20423
64.5%
unknown 9177
29.0%
telephone 2047
 
6.5%

Length

2023-01-12T08:55:20.324052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T08:55:20.617051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular 20423
64.5%
unknown 9177
29.0%
telephone 2047
 
6.5%

Most occurring characters

ValueCountFrequency (%)
l 63316
25.7%
u 29600
12.0%
n 29578
12.0%
e 26564
10.8%
c 20423
 
8.3%
a 20423
 
8.3%
r 20423
 
8.3%
o 11224
 
4.6%
k 9177
 
3.7%
w 9177
 
3.7%
Other values (3) 6141
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 246046
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 63316
25.7%
u 29600
12.0%
n 29578
12.0%
e 26564
10.8%
c 20423
 
8.3%
a 20423
 
8.3%
r 20423
 
8.3%
o 11224
 
4.6%
k 9177
 
3.7%
w 9177
 
3.7%
Other values (3) 6141
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 246046
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 63316
25.7%
u 29600
12.0%
n 29578
12.0%
e 26564
10.8%
c 20423
 
8.3%
a 20423
 
8.3%
r 20423
 
8.3%
o 11224
 
4.6%
k 9177
 
3.7%
w 9177
 
3.7%
Other values (3) 6141
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 246046
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 63316
25.7%
u 29600
12.0%
n 29578
12.0%
e 26564
10.8%
c 20423
 
8.3%
a 20423
 
8.3%
r 20423
 
8.3%
o 11224
 
4.6%
k 9177
 
3.7%
w 9177
 
3.7%
Other values (3) 6141
 
2.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.835466
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size247.4 KiB
2023-01-12T08:55:20.845052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median16
Q321
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.3370967
Coefficient of variation (CV)0.52648255
Kurtosis-1.067397
Mean15.835466
Median Absolute Deviation (MAD)7
Skewness0.087185435
Sum501145
Variance69.507182
MonotonicityNot monotonic
2023-01-12T08:55:21.100052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20 1909
 
6.0%
18 1612
 
5.1%
21 1445
 
4.6%
5 1373
 
4.3%
6 1348
 
4.3%
17 1344
 
4.2%
14 1283
 
4.1%
8 1281
 
4.0%
28 1276
 
4.0%
29 1241
 
3.9%
Other values (21) 17535
55.4%
ValueCountFrequency (%)
1 220
 
0.7%
2 900
2.8%
3 761
2.4%
4 1016
3.2%
5 1373
4.3%
6 1348
4.3%
7 1240
3.9%
8 1281
4.0%
9 1097
3.5%
10 360
 
1.1%
ValueCountFrequency (%)
31 460
 
1.5%
30 1082
3.4%
29 1241
3.9%
28 1276
4.0%
27 804
2.5%
26 761
2.4%
25 586
1.9%
24 305
 
1.0%
23 657
2.1%
22 640
2.0%

month
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
may
9669 
jul
4844 
aug
4333 
jun
3738 
nov
2783 
Other values (7)
6280 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters94941
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownov
2nd rowjul
3rd rowjul
4th rowjun
5th rowfeb

Common Values

ValueCountFrequency (%)
may 9669
30.6%
jul 4844
15.3%
aug 4333
13.7%
jun 3738
 
11.8%
nov 2783
 
8.8%
apr 2055
 
6.5%
feb 1827
 
5.8%
jan 977
 
3.1%
oct 512
 
1.6%
sep 410
 
1.3%
Other values (2) 499
 
1.6%

Length

2023-01-12T08:55:21.394052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may 9669
30.6%
jul 4844
15.3%
aug 4333
13.7%
jun 3738
 
11.8%
nov 2783
 
8.8%
apr 2055
 
6.5%
feb 1827
 
5.8%
jan 977
 
3.1%
oct 512
 
1.6%
sep 410
 
1.3%
Other values (2) 499
 
1.6%

Most occurring characters

ValueCountFrequency (%)
a 17376
18.3%
u 12915
13.6%
m 10011
10.5%
y 9669
10.2%
j 9559
10.1%
n 7498
7.9%
l 4844
 
5.1%
g 4333
 
4.6%
o 3295
 
3.5%
v 2783
 
2.9%
Other values (9) 12658
13.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 94941
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 17376
18.3%
u 12915
13.6%
m 10011
10.5%
y 9669
10.2%
j 9559
10.1%
n 7498
7.9%
l 4844
 
5.1%
g 4333
 
4.6%
o 3295
 
3.5%
v 2783
 
2.9%
Other values (9) 12658
13.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 94941
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 17376
18.3%
u 12915
13.6%
m 10011
10.5%
y 9669
10.2%
j 9559
10.1%
n 7498
7.9%
l 4844
 
5.1%
g 4333
 
4.6%
o 3295
 
3.5%
v 2783
 
2.9%
Other values (9) 12658
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 17376
18.3%
u 12915
13.6%
m 10011
10.5%
y 9669
10.2%
j 9559
10.1%
n 7498
7.9%
l 4844
 
5.1%
g 4333
 
4.6%
o 3295
 
3.5%
v 2783
 
2.9%
Other values (9) 12658
13.3%

duration
Real number (ℝ)

Distinct1454
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.11353
Minimum0
Maximum4918
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size247.4 KiB
2023-01-12T08:55:21.911052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1104
median180
Q3318.5
95-th percentile752
Maximum4918
Range4918
Interquartile range (IQR)214.5

Descriptive statistics

Standard deviation257.11897
Coefficient of variation (CV)0.99614681
Kurtosis19.487627
Mean258.11353
Median Absolute Deviation (MAD)93
Skewness3.1997657
Sum8168519
Variance66110.166
MonotonicityNot monotonic
2023-01-12T08:55:22.233050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 135
 
0.4%
124 130
 
0.4%
139 127
 
0.4%
88 127
 
0.4%
104 127
 
0.4%
112 125
 
0.4%
76 125
 
0.4%
135 124
 
0.4%
136 123
 
0.4%
166 123
 
0.4%
Other values (1444) 30381
96.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
2 3
 
< 0.1%
3 3
 
< 0.1%
4 11
 
< 0.1%
5 20
 
0.1%
6 32
0.1%
7 43
0.1%
8 60
0.2%
9 61
0.2%
10 49
0.2%
ValueCountFrequency (%)
4918 1
< 0.1%
3881 1
< 0.1%
3785 1
< 0.1%
3422 1
< 0.1%
3366 1
< 0.1%
3322 1
< 0.1%
3284 1
< 0.1%
3183 1
< 0.1%
3102 1
< 0.1%
3076 1
< 0.1%

campaign
Real number (ℝ)

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7656966
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size247.4 KiB
2023-01-12T08:55:22.563051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile8
Maximum63
Range62
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.11383
Coefficient of variation (CV)1.1258755
Kurtosis38.057995
Mean2.7656966
Median Absolute Deviation (MAD)1
Skewness4.8739349
Sum87526
Variance9.6959376
MonotonicityNot monotonic
2023-01-12T08:55:22.847052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 12262
38.7%
2 8798
27.8%
3 3858
 
12.2%
4 2442
 
7.7%
5 1245
 
3.9%
6 916
 
2.9%
7 518
 
1.6%
8 356
 
1.1%
9 236
 
0.7%
10 184
 
0.6%
Other values (35) 832
 
2.6%
ValueCountFrequency (%)
1 12262
38.7%
2 8798
27.8%
3 3858
 
12.2%
4 2442
 
7.7%
5 1245
 
3.9%
6 916
 
2.9%
7 518
 
1.6%
8 356
 
1.1%
9 236
 
0.7%
10 184
 
0.6%
ValueCountFrequency (%)
63 1
 
< 0.1%
55 1
 
< 0.1%
50 1
 
< 0.1%
44 1
 
< 0.1%
43 3
< 0.1%
41 1
 
< 0.1%
39 1
 
< 0.1%
38 3
< 0.1%
37 2
< 0.1%
36 1
 
< 0.1%

pdays
Real number (ℝ)

Distinct509
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.576042
Minimum-1
Maximum871
Zeros0
Zeros (%)0.0%
Negative25924
Negative (%)81.9%
Memory size247.4 KiB
2023-01-12T08:55:23.206048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile313
Maximum871
Range872
Interquartile range (IQR)0

Descriptive statistics

Standard deviation99.317592
Coefficient of variation (CV)2.5095383
Kurtosis7.1112947
Mean39.576042
Median Absolute Deviation (MAD)0
Skewness2.6423742
Sum1252463
Variance9863.9842
MonotonicityNot monotonic
2023-01-12T08:55:23.501051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 25924
81.9%
182 118
 
0.4%
92 100
 
0.3%
91 87
 
0.3%
183 85
 
0.3%
181 75
 
0.2%
370 65
 
0.2%
184 62
 
0.2%
95 54
 
0.2%
350 51
 
0.2%
Other values (499) 5026
 
15.9%
ValueCountFrequency (%)
-1 25924
81.9%
1 11
 
< 0.1%
2 25
 
0.1%
4 2
 
< 0.1%
5 7
 
< 0.1%
6 7
 
< 0.1%
7 6
 
< 0.1%
8 16
 
0.1%
9 8
 
< 0.1%
10 6
 
< 0.1%
ValueCountFrequency (%)
871 1
< 0.1%
854 1
< 0.1%
842 1
< 0.1%
838 1
< 0.1%
805 1
< 0.1%
804 1
< 0.1%
792 2
< 0.1%
791 1
< 0.1%
784 1
< 0.1%
782 1
< 0.1%

previous
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57427244
Minimum0
Maximum275
Zeros25924
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size247.4 KiB
2023-01-12T08:55:23.774053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum275
Range275
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4225289
Coefficient of variation (CV)4.2184313
Kurtosis5236.4116
Mean0.57427244
Median Absolute Deviation (MAD)0
Skewness49.302348
Sum18174
Variance5.868646
MonotonicityNot monotonic
2023-01-12T08:55:24.039051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 25924
81.9%
1 1921
 
6.1%
2 1481
 
4.7%
3 780
 
2.5%
4 501
 
1.6%
5 311
 
1.0%
6 188
 
0.6%
7 138
 
0.4%
8 81
 
0.3%
9 64
 
0.2%
Other values (28) 258
 
0.8%
ValueCountFrequency (%)
0 25924
81.9%
1 1921
 
6.1%
2 1481
 
4.7%
3 780
 
2.5%
4 501
 
1.6%
5 311
 
1.0%
6 188
 
0.6%
7 138
 
0.4%
8 81
 
0.3%
9 64
 
0.2%
ValueCountFrequency (%)
275 1
< 0.1%
58 1
< 0.1%
41 1
< 0.1%
38 1
< 0.1%
37 1
< 0.1%
35 1
< 0.1%
32 1
< 0.1%
30 1
< 0.1%
29 2
< 0.1%
28 1
< 0.1%

poutcome
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
unknown
25929 
failure
3362 
other
 
1288
success
 
1068

Length

Max length7
Median length7
Mean length6.9186021
Min length5

Characters and Unicode

Total characters218953
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowsuccess
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown 25929
81.9%
failure 3362
 
10.6%
other 1288
 
4.1%
success 1068
 
3.4%

Length

2023-01-12T08:55:24.330053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-12T08:55:24.650064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 25929
81.9%
failure 3362
 
10.6%
other 1288
 
4.1%
success 1068
 
3.4%

Most occurring characters

ValueCountFrequency (%)
n 77787
35.5%
u 30359
 
13.9%
o 27217
 
12.4%
k 25929
 
11.8%
w 25929
 
11.8%
e 5718
 
2.6%
r 4650
 
2.1%
f 3362
 
1.5%
a 3362
 
1.5%
i 3362
 
1.5%
Other values (5) 11278
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 218953
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 77787
35.5%
u 30359
 
13.9%
o 27217
 
12.4%
k 25929
 
11.8%
w 25929
 
11.8%
e 5718
 
2.6%
r 4650
 
2.1%
f 3362
 
1.5%
a 3362
 
1.5%
i 3362
 
1.5%
Other values (5) 11278
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 218953
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 77787
35.5%
u 30359
 
13.9%
o 27217
 
12.4%
k 25929
 
11.8%
w 25929
 
11.8%
e 5718
 
2.6%
r 4650
 
2.1%
f 3362
 
1.5%
a 3362
 
1.5%
i 3362
 
1.5%
Other values (5) 11278
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 218953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 77787
35.5%
u 30359
 
13.9%
o 27217
 
12.4%
k 25929
 
11.8%
w 25929
 
11.8%
e 5718
 
2.6%
r 4650
 
2.1%
f 3362
 
1.5%
a 3362
 
1.5%
i 3362
 
1.5%
Other values (5) 11278
 
5.2%

subscribed
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
False
27932 
True
3715 
ValueCountFrequency (%)
False 27932
88.3%
True 3715
 
11.7%
2023-01-12T08:55:24.887588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Interactions

2023-01-12T08:55:09.226052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:48.616474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:51.975471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:55.071472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:58.767472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:01.610472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:04.658054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:07.097056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:09.472053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:49.115474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:52.367472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:55.449472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:59.094472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:01.943473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:05.006054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:07.315053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:09.741054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:49.820474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:52.768474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:55.835472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:59.482473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:02.508472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:05.280052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:07.587053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:10.040051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:50.240472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:53.132473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:56.192472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:59.862473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:02.844473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:05.522061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:07.832057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:10.398054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:50.617474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:53.519473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:56.565472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:00.241473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:03.202530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:05.884053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:08.062055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:10.778056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:50.929473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:54.014474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:56.945472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:00.559474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:03.566527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:06.168059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:08.316055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:11.099052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:51.275475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:54.356472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:57.323473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:00.918472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:03.932055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:06.436055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:08.777053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:11.397054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:51.622474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:54.685473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:54:58.385473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:01.226473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:04.300056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:06.841055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-12T08:55:08.989055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-12T08:55:25.109578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2023-01-12T08:55:25.603578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-12T08:55:25.939579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-12T08:55:26.299573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-12T08:55:26.675575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-01-12T08:55:27.060577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-12T08:55:11.973052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-12T08:55:12.743051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDagejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomesubscribed
02611056admin.marriedunknownno1933nonotelephone19nov442-10unknownno
14057631unknownmarriedsecondaryno3nonocellular20jul912-10unknownno
21532027servicesmarriedsecondaryno891yesnocellular18jul2401-10unknownno
34396257managementdivorcedtertiaryno3287nonocellular22jun8671843successyes
42984231technicianmarriedsecondaryno119yesnocellular4feb3801-10unknownno
52939033managementsingletertiaryno0yesnocellular2feb1163-10unknownno
64044456retiredmarriedsecondaryno1044nonotelephone3jul3532-10unknownyes
74019450techniciansinglesecondaryno1811nonocellular8jun974-10unknownno
82982445blue-collardivorcedsecondaryno1951yesnocellular4feb6921-10unknownno
94467635admin.marriedsecondaryno1204nonocellular3sep7892-10unknownno
IDagejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomesubscribed
316372011044technicianmarriedsecondaryno5163nonocellular11aug482-10unknownno
316381630929blue-collarmarriedsecondaryno721yesnocellular23jul6441-10unknownno
3163927938servicessinglesecondaryno570yesnounknown5may752-10unknownno
316401210943managementsinglesecondaryno2968nonounknown20jun304-10unknownno
31641947637techniciansingletertiaryno1309nonounknown6jun4422-10unknownno
316423648329managementsingletertiaryno0yesnocellular12may1162-10unknownno
316434017853managementdivorcedtertiaryno380noyescellular5jun4382-10unknownyes
316441971032managementsingletertiaryno312nonocellular7aug373-10unknownno
316453855657technicianmarriedsecondaryno225yesnotelephone15may22733712failureno
316461415655managementdivorcedsecondaryno204yesnocellular11jul19732-10unknownyes